Speech recognition is realized mainly by a speech recognition model based on state modeling in the related art. For example, the speech recognition is based on a Hidden Markov Model (HMM for short). The HMM may be regarded as double random processes mathematically. One is an implicit random process which simulates changes of statistical properties of a speech signal by a Markov chain with a finite number of states. The other is a random process of an observed sequence related to each of the states of the Markov chain. In this modeling, one phoneme or one syllable is considered to be divided into a number of non-physical states, and then an output distribution of each of the non-physical states is described according to discrete or continuous Gaussian model or depth learning model. However, based on this modeling, confusion may occur and recognition performance is poor when the speech recognition is performed between two pronunciation units in the process of speech recognition.